It is often desirable to identify regions within an image which are suitable for the inclusion of text or figurative elements into the image. Such regions are known as open space, or alternatively, copy space, empty space, or dead space. Open space is typically one or more completely bounded sub-regions of an image whose color and spatial properties appear visually uniform. These sub-region boundaries may have either a regular or irregular shape. Images with large regions of regular, low contrast, smooth texture qualities are desirable regions to place such textual or figurative elements. An example use of open space would be in a photographic image used for the cover of a magazine, where the text used for the magazine title and description of feature articles must be placed in areas on the image where there exists a distinct absence of essential subject matter.
Open space can be characterized in terms of the spatial extent of the region, the location of the region relative to the entire image, and the dominant color and texture contained within the open space. Such characterizations are generally referred to as image metadata because such metadata is derived from the image. Specifically, the characterization of the extent, location, color and texture of the open space within an image is referred to as open space metadata.
Presently, the ability to detect and characterize open space in an image is a manual, subjective task which can produce limited results. It is a common practice to examine images with respect to their open space attributes in order to identify the proper image for a particular application. In the example of selecting an appropriate photographic image for the cover of a magazine, many images must be evaluated, not only for their open space attributes but also their content as appropriate for the magazine. A search of a very large image collection for images which meet the specific open space requirement, such as red regions across the upper 20% of the entire image, will produce only a limited number of candidate images from the collection due to the extensive, time consuming manual search required. Every image in the collection must be visually examined, even if it contains no open space whatsoever. Additionally, this entire process must be repeated for every open space search request.
The results of a manual search for images containing open space will be subjective, relying on the searcher's own mental concept of open space as it relates to the open space requirements set forth in the search request. The person requesting an image containing open space is not necessarily the person performing the search on the image collection. These two people may not share the same concept of open space as set forth in the search request, causing a mis-match in the open space search results. Additionally, each candidate image identified by manual inspection is equally weighted, with no quantitative ranking from best match of the search requirements to the worst match. These shortcomings may cause the search to produce results that may not identify adequate candidate images from the image collection even though they actually exist in the collection. Ideally, all images in the collection that meet a set of non-subjective open space criteria should be retrieved and presented to the user for review is some prescribed, non-subjective manner.
Therefore, there is a need for a method and system for objectively identifying and consistently characterizing the open space in images that avoids these problems.